MULTI-SOURCED INFORMATION TRUSTWORTHINESS ANALYSIS: APPLICATIONS AND THEORY
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In the era of Big Data, data entries, even describing the same objects or events, can come from a variety of sources. There are some sources that typically provide accurate information, but due to various reasons such as recording errors, device malfunction, background noise and intent to manipulate the data, some other sources may contain noisy or even erroneous information. Therefore, it is inevitable that information from multiple sources is conflicting with each other. To discover useful knowledge, which is usually deeply buried in those complicate multi-sourced data, we have to conduct information trustworthiness analysis on all available data sources. In this thesis, we propose a series of approaches of multi-sourced information trustworthiness analysis, including reliability-aware information integration and inconsistency detection to efficiently and effectively discover both trustworthy and untrustworthy information, respectively.